{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create, initialize, and print an integer variable that represents your age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'os' from 'C:\\\\Users\\\\acbart\\\\Anaconda3\\\\lib\\\\os.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sleep'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xd4VHXa//H3Te/N0CEEpFfBCAo2rIAFUfdZ1LW7qI/us25RUCyIrHVX1111ERWVXcu6FEVEARXFBgosJCG0UIQQeg095f79McP+ZmMCE5jMJJnP67rmYuZ7vjNzz8nwyZkzJ/cxd0dEROJHhVgXICIi0aXgFxGJMwp+EZE4o+AXEYkzCn4RkTij4BcRiTMKfhGROKPgFxGJMwp+EZE4UynWBRQmISHBk5KSYl2GiEiZsWDBgm3u3jCcuaUy+JOSkpg/f36syxARKTPM7Mdw52pXj4hInFHwi4jEGQW/iEicUfCLiMQZBb+ISJw5ZvCbWTUz+97MFpvZEjN7tJA5Vc3sn2aWYWbzzCwpZNn9wfHlZnZxZMsXEZHiCmeL/xBwnrv3AE4BBpjZ6QXm3ArsdPe2wHPAUwBm1hkYCnQBBgAvmVnFSBUvIiLFd8zg94C9wZuVg5eC52scDLwZvD4RON/MLDj+rrsfcvc1QAbQOyKVi4iUIz+s3cHYL1dF5bnC2sdvZhXNbBGwBZjl7vMKTGkOrAdw91xgN3BS6HhQZnCssOcYZmbzzWz+1q1bi/cqRETKqL2Hcnn4gzR+NvY73p63jv2Hc0v8OcMKfnfPc/dTgBZAbzPrWmCKFXa3o4wX9hzj3D3Z3ZMbNgzrr45FRMq0L1ds5eLn5vD3uT9yc78kPv71WdSoUvINFYr1DO6+y8y+ILC/Pi1kUSbQEsg0s0pAXWBHyPgRLYCsEylYRKSs27nvMI99lM7khRto26gWE+/oy6mt6kft+cM5qqehmdULXq8OXAAsKzBtKnBj8PrVwOfu7sHxocGjfloD7YDvI1W8iEhZ4u5MT93Ihc99ydRFWfzqvLZ89H9nRjX0Ibwt/qbAm8GjcSoA77n7NDMbDcx396nAa8DfzSyDwJb+UAB3X2Jm7wHpQC5wl7vnlcQLEREpzbbsOchDH6QxY8lmujWvy4Rb+tC5WZ2Y1GKBDfPSJTk52dWdU0TKA3fnXwsyGTMtnUO5+fzmwvbcdmZrKlWM7N/PmtkCd08OZ26pbMssIlIerN+xn/snp/J1xjZ6JzXgyau60aZhrViXpeAXEYm0vHznzW/X8syM5VSsYDx2RVeu651IhQqFHegYfQp+EZEIWrk5m+GTUli4bhfndmjI40O60axe9ViX9V8U/CIiEZCTl8/YL1bx188zqFm1In/++SkMPqUZgSYGpYuCX0TkBKVm7ubeiYtZtimbS7s3ZdTlXUioVTXWZRVJwS8icpwO5uTx3KcreGXOahJqVWXc9adyUZcmsS7rmBT8IiLHYe7q7dw/OZU12/ZxTe+WjBjYibrVK8e6rLAo+EVEiiH7YA5PfryMt+atI7FBDd6+rQ992ybEuqxiUfCLiIRp9rItPDAllc17DnLbma357UXto9JULdLKXsUiIlG2Y99hRn+4hPcXZdGuUS1eurMvPROj218nkhT8IiJFcHempWxk1NQl7D6Qw6/Pb8f/9j+ZqpXK9okEFfwiIoXYvOcgI6ek8enSzXRvUZe3ftmHjk1i01Qt0hT8IiIh3J1//rCeP0xfSk5ePiMHdeLmfkkRb6oWSwp+EZGgH7fvY8SkVL5bvZ3T2zTgySu7k5RQM9ZlRZyCX0TiXl6+8/o3a/jjzOVUrlCBx4d0Y+hpLUtNU7VIO2bwm1lLYALQBMgHxrn78wXm3AtcF/KYnYCG7r7DzNYC2UAekBtuv2gRkWhYvimb+yalsHj9Ls7v2IgxQ7rStG7paqoWaeFs8ecCv3P3hWZWG1hgZrPcPf3IBHd/BngGwMwuA37j7jtCHqO/u2+LZOEiIificG4+L32RwYuzM6hdrTLPDz2Fy3uUzqZqkXbM4Hf3jcDG4PVsM1sKNCdwOsXCXAO8E7EKRUQibNH6XQyfmMLyzdkMPqUZD1/amZNKcVO1SCvWPn4zSwJ6AvOKWF4DGADcHTLswEwzc+Bldx93XJWKiJygA4fzeHbWcl77eg2NalfjtRuTOb9T41iXFXVhB7+Z1QImAfe4+54ipl0GfFNgN08/d88ys0bALDNb5u5zCnn8YcAwgMTExLBfgIhIOL5dtY0Rk1JZt2M/1/ZJZMTAjtSpVjaaqkVaWMFvZpUJhP5b7j75KFOHUmA3j7tnBf/dYmZTgN7AT4I/+ElgHAROth5W9SIix7DnYA5PTF/GO9+vo9VJNXjnl6dzxsknxbqsmArnqB4DXgOWuvuzR5lXFzgH+EXIWE2gQvC7gZrARcDoE65aRCQMn6ZvZuT7qWzNPsSws9vwmwvaU71K2W63EAnhbPH3A64HUs1sUXDsASARwN3HBseGADPdfV/IfRsDU4LfklcC3nb3TyJRuIhIUbbvPcSjH6YzdXEWHZvUZtz1yfRoWS/WZZUa4RzV8zVwzOOb3P0N4I0CY6uBHsdZm4hIsbg7UxdnMWrqEvYeyuU3F7TnznNPpkql8tNuIRL0l7siUi5k7TrAg++n8fmyLZzSsh5PX92d9o1rx7qsUknBLyJlWn6+884P63hi+jLy8p2HLu3MTX2TqFhO2y1EgoJfRMqsNdv2MWJSCvPW7KBf25N4Ykh3Ek+qEeuySj0Fv4iUObl5+Yz/Zg1/mrmCKpUq8NRV3fif5JZx0W4hEhT8IlKmLN24h+GTUkjJ3M2FnRsz5oquNK5TLdZllSkKfhEpEw7l5vHi5xm89MUq6lavzAvX9uSSbk21lX8cFPwiUuotXLeT4RNTWLllL1f2bM5Dl3amfs0qsS6rzFLwi0iptf9wLn+csYLXv11D0zrVeP3m0+jfoVGsyyrzFPwiUip9k7GNEZNTWL/jANef3or7BnSgdpw2VYs0Bb+IlCq7D+Tw+EdL+ef89bROqMk/h51Onzbx3VQt0hT8IlJqzFiyiYfeT2P7vsPccc7J3HNBO6pVVlO1SFPwi0jMbc0+xKipS/godSOdmtbhtRtPo1uLurEuq9xS8ItIzLg7U/69gdHT0tl/KI/fX9Se2885mcoV1VStJCn4RSQmNuw6wMgpqXyxfCu9EgNN1do2UlO1aFDwi0hU5ec7b837kSc/XoYDoy7rzPVnqKlaNCn4RSRqVm3dy4hJKfywdidntUvg8SHdaNlATdWi7Zg70syspZnNNrOlZrbEzH5dyJxzzWy3mS0KXh4OWTbAzJabWYaZjYj0CxCR0i83L5+Xvshg4PNfsXxTNs9c3Z0Jt/RW6MdIOFv8ucDv3H2hmdUGFpjZLHdPLzDvK3e/NHTAzCoCLwIXApnAD2Y2tZD7ikg5tSRrN8MnpZC2YQ8DujRh9OAuNFJTtZgK59SLG4GNwevZZrYUaA6EE969gYzgKRgxs3eBwWHeV0TKsIM5efz185WM/XI19WtU4W/X9WJgt6axLkso5j5+M0sCegLzCll8hpktBrKA37v7EgK/INaHzMkE+hxXpSJSZiz4cQf3TUxh1dZ9XNWrBQ9d2ol6NdRUrbQIO/jNrBYwCbjH3fcUWLwQaOXue81sEPA+0I7CT9LuRTz+MGAYQGJiYrhliUgpsu9QLs/MWM6b362lWd3qvHlLb85p3zDWZUkBYQW/mVUmEPpvufvkgstDfxG4+3Qze8nMEghs4bcMmdqCwCeCn3D3ccA4gOTk5EJ/OYhI6TVnxVbun5xK1u4D3HB6K+4d0JFaVXXgYGl0zJ+KBc5y8Bqw1N2fLWJOE2Czu7uZ9SZwtNB2YBfQzsxaAxuAocC1kSpeRGJv1/7DjPloKRMXZNKmYU3eu/0MTktqEOuy5CjC+XXcD7geSDWzRcGxB4BEAHcfC1wN3GlmucABYKi7O5BrZncDM4CKwPjgvn8RKQc+Tt3IQx8sYef+w9zV/2R+dZ6aqpUFFsjn0iU5Odnnz58f6zJEpAhbsg/yyAdL+DhtE12a1eHpq7vTpZmaqsWSmS1w9+Rw5moHnIiEzd2ZuCCTMR8t5UBOHvcN6MAvz2qjpmpljIJfRMKyfsd+HpiSylcrt3FaUn2evKo7JzesFeuy5Dgo+EXkqPLznQnfreXpGcsxYPTgLvyiTysqqKlamaXgF5EiZWzJZvikVBb8uJOz2zfk8SFdaVFf/XXKOgW/iPxETl4+4+as5vlPV1KjakX+9LMeXNmrOYGju6WsU/CLyH9J27Cb+yamkL5xD5d0a8qoy7vQsHbVWJclEaTgFxEg0FTt+c9WMm7OahrUrMLYX5zKgK5NYl2WlAAFv4jw/ZodjJiUwupt+/if5BaMHNSZujUqx7osKSEKfpE4tvdQLk99vIy/z/2RFvWr849b+3Bmu4RYlyUlTMEvEqdmL9/CyMmpbNxzkJv7JfH7izpQU03V4oJ+yiJxZue+wzw2LZ3J/95A20a1mHhHX05tVT/WZUkUKfhF4oS7Mz11E49MTWPX/hz+77y23HVeW6pWUlO1eKPgF4kDm/cc5KH305iZvpluzesy4ZY+dG5WJ9ZlSYwo+EXKMXfnvfnrGfPRUg7n5nP/wI7cemZrKqmpWlxT8IuUU+u27+f+KSl8k7Gd3q0b8OSV3WijpmqCgl+k3MnLd974di1/nLGcihWMMVd05dreiWqqJv8RzqkXWwITgCZAPjDO3Z8vMOc6YHjw5l7gTndfHFy2FsgG8oDccE8UICLFt3JzNvdNSuHf63bRv0ND/jCkG83qVY91WVLKhLPFnwv8zt0XmlltYIGZzXL39JA5a4Bz3H2nmQ0kcNL0PiHL+7v7tsiVLSKhDufmM/bLVfz185XUqlqJP//8FAaf0kxN1aRQxwx+d98IbAxezzazpUBzID1kzrchd5kLtIhwnSJShMXrdzF8UgrLNmVzWY9mPHJZZxJqqamaFK1Y+/jNLAnoCcw7yrRbgY9Dbjsw08wceNndxxXx2MOAYQCJiYnFKUskLh04nMefP13BK1+tpmHtqrxyQzIXdm4c67KkDAg7+M2sFjAJuMfd9xQxpz+B4D8zZLifu2eZWSNglpktc/c5Be8b/IUwDgInWy/GaxCJO3NXb2fEpBTWbt/PNb1bMmJgJ+pWV1M1CU9YwW9mlQmE/lvuPrmIOd2BV4GB7r79yLi7ZwX/3WJmU4DewE+CX0SOLftgDk9+vIy35q0jsUEN3r6tD33bqqmaFE84R/UY8Bqw1N2fLWJOIjAZuN7dV4SM1wQqBL8bqAlcBIyOSOUicebzZZsZOSWNzXsOctuZrfntRe2pUUVHZEvxhfOu6QdcD6Sa2aLg2ANAIoC7jwUeBk4CXgoeRXDksM3GwJTgWCXgbXf/JKKvQKSc2773EKOnpfPBoizaN67FS9f1pWeimqrJ8QvnqJ6vgaMeE+butwG3FTK+Guhx3NWJxDF358OUjYyauoTsgzn8+vx23NW/LVUqqd2CnBh9ThQphTbtPsiD76fy6dIt9GhRl6eu7kPHJmqqJpGh4BcpRdydd39Yz+MfLSUnP5+Rgzpxy5mtqah2CxJBCn6RUmLttn3cPzmV71Zv5/Q2DXjyyu4kJdSMdVlSDin4RWIsL98Z//Ua/jRrOZUrVOCJK7sx9LSWarcgJUbBLxJDyzdlc9/ExSzO3M0FnRox5opuNKlbLdZlSTmn4BeJgcO5+bw4O4OXvsigdrXK/OWanlzWvam28iUqFPwiUbZo/S7um7iYFZv3MviUZjxyWRca1KwS67Ikjij4RaJk/+Fcnp25gvHfrKFR7Wq8dmMy53dSUzWJPgW/SBR8m7GNEZNTWbdjP9f1SWT4wI7UqaamahIbCn6RErT7QA5PTF/Kuz+sJ+mkGrw77HROb3NSrMuSOKfgFykhs9I38+D7qWzNPsTtZ7fhngvaU71KxViXJaLgF4m0bXsPMWrqEqalbKRjk9q8ckMy3VvUi3VZIv+h4BeJEHfng0VZPPrhEvYeyuW3F7bnjnNOVlM1KXUU/CIRkLXrACOnpDJ7+VZOaVmPp6/uTvvGtWNdlkihFPwiJyA/33nr+3U89fEy8vKdhy7tzE19k9RUTUq1Y34GNbOWZjbbzJaa2RIz+3Uhc8zM/mJmGWaWYma9QpbdaGYrg5cbI/0CRGJlzbZ9DH1lLg+9n0aPlnWZcc/Z3KpOmlIGhLPFnwv8zt0XmlltYIGZzXL39JA5A4F2wUsf4G9AHzNrADwCJAMevO9Ud98Z0VchEkW5efm8+vUanpu1giqVKvD0Vd35WXILtVuQMiOcM3BtBDYGr2eb2VKgORAa/IOBCe7uwFwzq2dmTYFzgVnuvgPAzGYBA4B3IvoqRKIkPWsPwyelkLphNxd2bsyYK7rSuI6aqknZUqx9/GaWBPQE5hVY1BxYH3I7MzhW1LhImXIoN48XPs/gb1+sol6Nyrx4bS8GdWuirXwpk8IOfjOrBUwC7nH3PQUXF3IXP8p4YY8/DBgGkJiYGG5ZIiVuwY87GT4phYwtexnSszkPX9qZ+mqqJmVYWMFvZpUJhP5b7j65kCmZQMuQ2y2ArOD4uQXGvyjsOdx9HDAOIDk5udBfDiLRtO9QLn+cuZw3vl1L0zrVeP3m0+jfoVGsyxI5YccMfgt8ln0NWOruzxYxbSpwt5m9S+DL3d3uvtHMZgCPm1n94LyLgPsjULdIifpq5Vbun5xK5s4D3HBGK+4b0JFaVXX0s5QP4byT+wHXA6lmtig49gCQCODuY4HpwCAgA9gP3BxctsPMHgN+CN5v9JEvekVKo937c/jD9HTem59J64SavHf7GfRu3SDWZYlEVDhH9XxN4fvqQ+c4cFcRy8YD44+rOpEo+iRtEw99kMaOfYe589yT+fX57ahWWU3VpPzRZ1eJe1uzA03VPkrdSKemdRh/42l0a1E31mWJlBgFv8Qtd2fywg2MnpbOgcN53HtxB4ad3YbKFdVUTco3Bb/Epcyd+3lgShpzVmzl1Fb1eeqq7rRtVCvWZYlEhYJf4kp+vvOPeT/y1MfLcGDUZZ254YwkKqi/jsQRBb/EjVVb9zJiUgo/rN3JWe0SeHxIN1o2qBHrskSiTsEv5V5OXj6vfLWaP3+6kmqVKvDM1d25+lQ1VZP4peCXci1tw26GT0phSdYeBnRpwugrutCotpqqSXxT8Eu5dDAnj798tpKX56ymfo0q/O26Xgzs1jTWZYmUCgp+KXfmr93BfZNSWL11H1ef2oIHL+lEvRpqqiZyhIJfyo29h3J55pNlTJj7I83qVmfCLb05u33DWJclUuoo+KVc+HLFVh6YnErW7gPceEYS917cgZpqqiZSKP3PkDJt1/7DPDZtKZMWZtKmYU3+dfsZJCepqZrI0Sj4pcyanrqRhz9IY+f+HO7qfzK/Ok9N1UTCoeCXMmfLnoM8/MESPlmyiS7N6vDmLb3p0kxN1UTCpeCXMsPd+deCTMZMS+dgbj7DB3Tkl2e1ppKaqokUi4JfyoT1O/bzwJRUvlq5jdOS6vPkVd05uaGaqokcj3BOvTgeuBTY4u5dC1l+L3BdyON1AhoGz761FsgG8oBcd0+OVOESH/LynQnfreWZGcsx4LHBXbiuTys1VRM5AeFs8b8BvABMKGyhuz8DPANgZpcBvylwesX+7r7tBOuUOJSxJZv7JqawcN0uzmnfkD8M6UqL+mqqJnKiwjn14hwzSwrz8a4B3jmRgkRy8vJ5+ctV/OWzDGpUrciz/9ODIT2bq6maSIREbB+/mdUABgB3hww7MNPMHHjZ3cdF6vmkfErN3M29ExezbFM2l3RvyqjLutCwdtVYlyVSrkTyy93LgG8K7Obp5+5ZZtYImGVmy9x9TmF3NrNhwDCAxMTECJYlZcHBnDz+/OlKXvlqNQ1qVuHl60/l4i5NYl2WSLkUyeAfSoHdPO6eFfx3i5lNAXoDhQZ/8NPAOIDk5GSPYF1Sys1bvZ0Rk1NZs20fP09uyQODOlG3RuVYlyVSbkUk+M2sLnAO8IuQsZpABXfPDl6/CBgdieeT8iH7YA5Pf7Kcv8/9kRb1q/OPW/twZruEWJclUu6FczjnO8C5QIKZZQKPAJUB3H1scNoQYKa77wu5a2NgSvALuUrA2+7+SeRKl7Js9rItjJySysY9B7mlX2t+f3F7alTRn5WIREM4R/VcE8acNwgc9hk6throcbyFSfm0Y99hHpuWzpR/b6Bdo1pMvKMvp7aqH+uyROKKNrEkKtydj1I38sgHS9h9IIf/O68td53XlqqV1FRNJNoU/FLiNu85yIPvpzErfTPdmtflH7f1oVPTOrEuSyRuKfilxLg7781fz5iPlnI4N5/7B3bk1jPVVE0k1hT8UiLWbd/PiMkpfLtqO71bN+Cpq7rTOqFmrMsSERT8EmF5+c7r36zhTzNXULGCMeaKrlzbO1FN1URKEQW/RMyKzYGmaovW7+K8jo0Yc0VXmtWrHuuyRKQABb+csMO5+fzti1W8MHsltapW4vmhp3B5j2ZqqiZSSin45YQsXr+L4ZNSWLYpm8t6NGPUZZ05qZaaqomUZgp+OS4HDufx3KcrePWr1TSsXZVXbkjmws6NY12WiIRBwS/F9t2q7dw/OYW12/dzTe+W3D+oE3WqqamaSFmh4Jew7TmYw5MfL+PteetIbFCDt2/rQ9+2aqomUtYo+CUsny3dzMgpaWzJPsgvz2rNby/sQPUqarcgUhYp+OWotu89xKMfpjN1cRYdGtdm7PWnckrLerEuS0ROgIJfCuXuTF2cxaMfppN9MId7LmjH/57bliqV1G5BpKxT8MtPbNx9gAenpPHZsi30aFmPp6/qTocmtWNdlohEiIJf/iM/33n3h/U8MX0pOfn5PHhJJ27u15qKarcgUq4c83O7mY03sy1mllbE8nPNbLeZLQpeHg5ZNsDMlptZhpmNiGThEllrt+3j2lfn8sCUVLo2r8uMe87mtrPaKPRFyqFwtvjfAF4AJhxlzlfufmnogJlVBF4ELgQygR/MbKq7px9nrVICcvPyGR9sqlalYgWevLIbPz+tpdotiJRj4Zx6cY6ZJR3HY/cGMoKnYMTM3gUGAwr+UmLZpj0Mn5jC4szdXNCpEWOu6EaTutViXZaIlLBI7eM/w8wWA1nA7919CdAcWB8yJxPoU9QDmNkwYBhAYmJihMqSwhzKzePF2at4aXYGdatX5q/X9OTS7k21lS8SJyIR/AuBVu6+18wGAe8D7YDCUsSLehB3HweMA0hOTi5ynpyYf6/byfBJKazYvJcrTmnGw5d1oUHNKrEuS0Si6ISD3933hFyfbmYvmVkCgS38liFTWxD4RCAxsP9wLn+auYLx36yhSZ1qjL8pmfM6qqmaSDw64eA3sybAZnd3M+tN4Eih7cAuoJ2ZtQY2AEOBa0/0+aT4vsnYxojJKazfcYDr+iQyYmBHaqupmkjcOmbwm9k7wLlAgpllAo8AlQHcfSxwNXCnmeUCB4Ch7u5ArpndDcwAKgLjg/v+JUp2H8jhielLefeH9SSdVIN3h53O6W1OinVZIhJjFsjo0iU5Odnnz58f6zLKtJlLNvHg+2ls23uIX57dht9c0J5qldVUTaS8MrMF7p4czlz95W45s23vIUZNXcK0lI10bFKbV29MpnsLNVUTkf9PwV9OuDvvL9rAox+ms/9QHr+7sD23n3OymqqJyE8o+MuBrF0HGDklldnLt9IzMdBUrV1jNVUTkcIp+Muw/Hznre/X8eT0peQ7PHxpZ27sm6T+OiJyVAr+Mmr11r2MmJTK92t3cGbbBJ64shstG9SIdVkiUgYo+MuY3Lx8Xv16Dc/NWkGVShV4+qru/Cy5hdotiEjYFPxlSHrWHu6btJi0DXu4qHNjHruiK43rqKmaiBSPgr8MOJSbxwufZ/C3L1ZRr0ZlXry2F4O6NdFWvogcFwV/Kbfgxx0Mn5RKxpa9XNmrOQ9d0pn6aqomIidAwV9K7TuUyzMzlvPmd2tpVrc6b9x8Gud2aBTrskSkHFDwl0JfrdzK/ZNTydx5gBvOaMV9AzpSq6p+VCISGUqTUmT3/hzGfJTOvxZk0iahJu/dfga9WzeIdVkiUs4o+EuJT9I28dAHaezYd5g7zz2ZX5/fTk3VRKREKPhjbEv2QUZNXcL01E10blqH1286ja7N68a6LBEpxxT8MeLuTFq4gcempXMgJ497L+7AsLPbULmimqqJSMlS8MdA5s79PDAljTkrtnJqq/o8dVV32jaqFeuyRCROhHMGrvHApcAWd+9ayPLrgOHBm3uBO919cXDZWiAbyANywz1JQHmVn+/8fe6PPPXJMgAevbwL15/eigpqqiYiURTOFv8bwAvAhCKWrwHOcfedZjYQGAf0CVne3923nVCV5cCqrXsZPjGF+T/u5Kx2CTw+RE3VRCQ2jhn87j7HzJKOsvzbkJtzgRYnXlb5kZOXz7g5q3n+s5VUr1yRP/6sB1f1aq52CyISM5Hex38r8HHIbQdmmpkDL7v7uKLuaGbDgGEAiYmJES4rNtI27Gb4pBSWZO1hULcmjLq8C41qq6maiMRWxILfzPoTCP4zQ4b7uXuWmTUCZpnZMnefU9j9g78UxkHgZOuRqisWDubk8ZfPVvLynNXUr1GFsb/oxYCuTWNdlogIEKHgN7PuwKvAQHfffmTc3bOC/24xsylAb6DQ4C8vfli7g+ETU1i9bR8/O7UFD17Smbo1Kse6LBGR/zjh4DezRGAycL27rwgZrwlUcPfs4PWLgNEn+nyl1d5DuTz9yTImfPcjzetVZ8ItvTm7fcNYlyUi8hPhHM75DnAukGBmmcAjQGUAdx8LPAycBLwU/MLyyGGbjYEpwbFKwNvu/kkJvIaY+3LFVh6YnErW7gPc1DeJey/uQE01VRORUiqco3quOcby24DbChlfDfQ4/tJKv137DzN6WjqTF27g5IY1+dftZ5CcpKZqIlK6abP0OLg7H6dt4uEP0ti1P4e7+7fl7vPaqqmaiJQJCv5i2rLnIA99kMaMJZvp2rwOb97Smy7N1FRNRMoOBX+Y3J1/LchkzLT2N2WNAAAI1ElEQVR0DubmM3xAR355VmsqqamaiJQxCv4wrN+xn/snp/J1xjZ6JzXgyau60aahmqqJSNmk4D+KvHxnwndrefqT5VQweGxwF67ro6ZqIlK2KfiLkLElm/smprBw3S7Oad+Qx6/sRvN61WNdlojICVPwF5CTl8/YL1bx188zqFG1Is/9vAdXnKKmaiJSfij4Q6Rm7ubeiYtZtimbS7o35dHLu5BQq2qsyxIRiSgFP4Gmas99uoJX5qwmoVZVXr7+VC7u0iTWZYmIlIi4D/55q7czYnIqa7bt4+fJLXngkk7Ura6maiJSfsVt8GcfzOGpT5bxj7nraNmgOm/d1od+bRNiXZaISImLy+CfvWwLI6eksnHPQW49szW/u6g9NarE5aoQkTgUV2m3Y99hHpuWzpR/b6Bdo1pMurMvvRLrx7osEZGoiovgd3empWxk1NQl7D6Qw/+d3467+p9M1UpqqiYi8afcB//mPQcZOSWNT5dupnuLuvzjtj50alon1mWJiMRMWB3GzGy8mW0xs7QilpuZ/cXMMswsxcx6hSy70cxWBi83RqrwY3F33v1+HRc8+yVfrdzKA4M6MvnOvgp9EYl74W7xvwG8AEwoYvlAoF3w0gf4G9DHzBoQOGNXMuDAAjOb6u47T6ToY1m3fT8jJqfw7art9GndgKeu6k5SQs2SfEoRkTIjrOB39zlmlnSUKYOBCe7uwFwzq2dmTQmcsnGWu+8AMLNZwADgnRMpuih5+c7r36zhjzOXU6lCBf4wpCvXnJaopmoiIiEitY+/ObA+5HZmcKyo8YjbvT+HG1//nkXrd3Fex0b8YUhXmtZVUzURkYIiFfyFbVL7UcZ/+gBmw4BhAImJicUuoE71SrQ6qQY390vi8h7N1FRNRKQIkTp9VCbQMuR2CyDrKOM/4e7j3D3Z3ZMbNmxY7ALMjOeH9mSwOmmKiBxVpIJ/KnBD8Oie04Hd7r4RmAFcZGb1zaw+cFFwTEREYiSsXT1m9g6BL2oTzCyTwJE6lQHcfSwwHRgEZAD7gZuDy3aY2WPAD8GHGn3ki14REYmNcI/queYYyx24q4hl44HxxS9NRERKQqR29YiISBmh4BcRiTMKfhGROKPgFxGJMwp+EZE4Y4EDckoXM9sK/Hicd08AtkWwnEhRXcWjuopHdRVPeayrlbuH9devpTL4T4SZzXf35FjXUZDqKh7VVTyqq3jivS7t6hERiTMKfhGROFMeg39crAsoguoqHtVVPKqreOK6rnK3j19ERI6uPG7xi4jIUZSZ4DezAWa2PHhC9xGFLK9qZv8MLp8XeqpIM7s/OL7czC6Ocl2/NbP04EnoPzOzViHL8sxsUfAyNcp13WRmW0Oe/7aQZTea2crg5cYo1/VcSE0rzGxXyLKSXF/jzWyLmaUVsdzM7C/BulPMrFfIspJcX8eq67pgPSlm9q2Z9QhZttbMUoPra36U6zrXzHaH/LweDll21PdACdd1b0hNacH3VIPgspJcXy3NbLaZLTWzJWb260LmRO895u6l/gJUBFYBbYAqwGKgc4E5/wuMDV4fCvwzeL1zcH5VoHXwcSpGsa7+QI3g9TuP1BW8vTeG6+sm4IVC7tsAWB38t37wev1o1VVg/q+A8SW9voKPfTbQC0grYvkg4GMCZ5U7HZhX0usrzLr6Hnk+YOCRuoK31wIJMVpf5wLTTvQ9EOm6Csy9DPg8SuurKdAreL02sKKQ/5NRe4+VlS3+3kCGu69298PAuwRO8B5qMPBm8PpE4Hwzs+D4u+5+yN3XEDhnQO9o1eXus919f/DmXAJnIStp4ayvolwMzHL3He6+E5gFDIhRXdcA70TouY/K3ecARztXxGBgggfMBeqZWVNKdn0dsy53/zb4vBC991c466soJ/LejHRd0Xx/bXT3hcHr2cBSfnr+8ai9x8pK8Idz0vb/zHH3XGA3cFKY9y3JukLdSuA3+hHVzGy+mc01sysiVFNx6roq+JFyopkdOUVmqVhfwV1irYHPQ4ZLan2Fo6jaS3J9FVfB95cDM81sgQXOaR1tZ5jZYjP72My6BMdKxfoysxoEwnNSyHBU1pcFdkP3BOYVWBS191ikTrZe0sI5afsJn/D9OBTnZPK/AJKBc0KGE909y8zaAJ+bWaq7r4pSXR8C77j7ITO7g8CnpfPCvG9J1nXEUGCiu+eFjJXU+gpHLN5fYTOz/gSC/8yQ4X7B9dUImGVmy4JbxNGwkEALgb1mNgh4H2hHKVlfBHbzfOP/fUbAEl9fZlaLwC+be9x9T8HFhdylRN5jZWWLP5yTtv9njplVAuoS+MgX9gnfS6guzOwCYCRwubsfOjLu7lnBf1cDXxDYCohKXe6+PaSWV4BTw71vSdYVYigFPoaX4PoKR1G1l+T6CouZdQdeBQa7+/Yj4yHrawswhcjt4jwmd9/j7nuD16cDlc0sgVKwvoKO9v4qkfVlZpUJhP5b7j65kCnRe4+VxBcZkb4Q+GSymsBH/yNfCHUpMOcu/vvL3feC17vw31/uriZyX+6GU1dPAl9mtSswXh+oGryeAKwkQl9yhVlX05DrQ4C5/v+/SFoTrK9+8HqDaNUVnNeBwBdtFo31FfIcSRT9ZeUl/PcXb9+X9PoKs65EAt9b9S0wXhOoHXL9W2BAFOtqcuTnRyBA1wXXXVjvgZKqK7j8yEZhzWitr+BrnwD8+ShzovYei9jKLukLgW+8VxAI0ZHBsdEEtqIBqgH/Cv4n+B5oE3LfkcH7LQcGRrmuT4HNwKLgZWpwvC+QGnzjpwK3RrmuJ4AlweefDXQMue8twfWYAdwczbqCt0cBTxa4X0mvr3eAjUAOgS2sW4E7gDuCyw14MVh3KpAcpfV1rLpeBXaGvL/mB8fbBNfV4uDPeWSU67o75P01l5BfTIW9B6JVV3DOTQQO+Ai9X0mvrzMJ7J5JCflZDYrVe0x/uSsiEmfKyj5+ERGJEAW/iEicUfCLiMQZBb+ISJxR8IuIxBkFv4hInFHwi4jEGQW/iEic+X+QrKP+hTXDggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x201a74257f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([1,2,3])\n",
    "def summate(a_list):\n",
    "    s = 0\n",
    "    for x in a_list:\n",
    "        s += x\n",
    "    return s\n",
    "summate([1,2,3])\n",
    "'name'\n",
    "'travel home'\n",
    "'duration'\n",
    "'fun_factor'\n",
    "'homework'\n",
    "'visit friends'\n",
    "'dinner with grandma'\n",
    "'sleep'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'0'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-37891c5daebd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"0\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: '0'"
     ]
    }
   ],
   "source": [
    "test = {\"alpha\": 5}\n",
    "test[input(\"0\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "// Convert Notebook cells to a string of Python code\n",
       "var makePython = function(cell) {\n",
       "    if (cell.cell_type == \"code\") {\n",
       "        // Code is embedded unchanged, unless it is magic\n",
       "        var source = cell.get_text();\n",
       "        if (source.startsWith('%')) {\n",
       "            // Skip magic\n",
       "            return '';\n",
       "        } else {\n",
       "            return source;\n",
       "        }\n",
       "    } else if (cell.cell_type == \"markdown\" ||\n",
       "               cell.cell_type == \"raw\") {\n",
       "        // Markdown and text is wrapped in a string.\n",
       "        var escaped_text = cell.get_text().replace(/'''/g, \"\\\\'\\\\'\\\\'\");\n",
       "        return \"'''\"+escaped_text+\"'''\";\n",
       "    }\n",
       "}\n",
       "var isUsable = function(cell) {\n",
       "    return cell.cell_type == \"code\" ||\n",
       "           cell.cell_type == \"markdown\" ||\n",
       "           cell.cell_type == \"raw\";\n",
       "}\n",
       "var cells = Jupyter.notebook.get_cells();\n",
       "var source_code = cells.filter(isUsable).map(makePython).join(\"\\n\");\n",
       "source_code = JSON.stringify(source_code);\n",
       "console.log(source_code);\n",
       "// Start constructing the feedback code (which will be Python).\n",
       "var on_run_code = [];\n",
       "on_run_code.push(\"student_code=\"+source_code);\n",
       "\n",
       "// While we are accessing the server, recolor the last cell a little.\n",
       "var last = null;\n",
       "if (cells.length > 0) {\n",
       "    last = cells[cells.length-1];\n",
       "    $(last.element).animate({\"background-color\": \"#E0E6FF\"}, 1000);\n",
       "}\n",
       "\n",
       "on_run_code.push(\"from pedal.plugins.grade_magic import blockpy_grade\");\n",
       "on_run_code.push('import json')\n",
       "on_run_code.push('inputs = json.dumps(\"alpha\")')\n",
       "on_run_code.push(\"print(blockpy_grade(1, student_code, inputs))\");\n",
       "\n",
       "on_run_code = on_run_code.join(\"\\n\");\n",
       "console.log(on_run_code);\n",
       "var kernel = IPython.notebook.kernel;\n",
       "if (kernel !== null) {\n",
       "    var t = kernel.execute(on_run_code, { 'iopub' : {'output' : function(x) {\n",
       "        if (x.msg_type == \"error\") {\n",
       "            // If this was an error, show the traceback properly.\n",
       "            if (last !== null) {\n",
       "                last.output_area.append_error(x.content);\n",
       "                console.error(x);\n",
       "            } else {\n",
       "                console.error(\"Could not append to final cell.\", x);\n",
       "            }\n",
       "        } else if (!x.content.data && x.content.text) {\n",
       "            // If it was valid data, we show it as HTML.\n",
       "            console.log(x);\n",
       "            element.html(x.content.text.replace(/\\n/g, \"<br>\"));\n",
       "        } else {\n",
       "            // I'm not sure what it is - better dump it on the console.\n",
       "            console.log(x);\n",
       "        }\n",
       "        // Decolor the last cell if it was there.\n",
       "        if (last !== null) {\n",
       "            last = cells[cells.length-1];\n",
       "            $(last.element).animate({\"background-color\": \"white\"}, 1000);\n",
       "        }\n",
       "    }}});\n",
       "}"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%grade_blockpy 1,alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Anaconda3]",
   "language": "python",
   "name": "conda-env-Anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  },
  "student_mode": false
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
